Systems and methods for image-based nerve fiber extraction

ABSTRACT

The present disclosure provides methods and systems for image-based nerve fiber extraction. The methods may include obtaining an anatomical image of a subject and a diffusion image of the subject. The subject may include at least one region of interest (ROI) that relates to extraction of at least one target nerve fiber in the subject. The methods may further include determining, based on the anatomical image, the at least one ROI in the diffusion image; and extracting, from the diffusion image, at least one of the at least one target nerve fiber based on the at least one ROI.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of International Application No.PCT/CN2020/137836, filed on Dec. 20, 2020, which designates the UnitedStates of America and claims priority to Chinese Application No.201911326191.X, filed on Dec. 20, 2019, the entire contents of which arehereby incorporated by reference.

TECHNICAL FIELD

The present disclosure generally relates to image processing, and moreparticularly, relates to systems and methods for image-based nerve fiberextraction.

BACKGROUND

Diffusion tensor imaging (DTI) is an advanced MRI (Magnetic ResonanceImaging, MRI) technology that can allow non-invasive observation of themacroscopic and microscopic anatomical structures of nerve fibers, suchas brain white matter fibers. DTI utilizes the diffusion of watermolecules to reveal microscopic details about tissue architecture.

Conventionally, when performing DTI imaging methods in the prior art, aDTI processing software is usually used to track the nerve fibers of thebrain. The doctor manually draws or labels one or more regions ofinterest (ROIs) on the DTI image based on the anatomical data, and thenone or more nerve fibers of interest are extracted based on the manuallydrawn ROI(s). However, such a conventional process of obtaining theROI(s) of the DTI image by manual drawing is relatively cumbersome,which in turn leads to low efficiency of neural fiber tracking in theROI. It is desired to develop more efficient systems and methods fordetermining the ROI(s) for extracting one or more nerve fibers ofinterest.

SUMMARY

According to an aspect of the present disclosure, a method forimage-based nerve fiber extraction is provided. The method may beimplemented on a computing device having at least one processor and atleast one non-transitory storage medium. The method may includeobtaining an anatomical image of a subject and a diffusion image of thesubject, the subject including at least one region of interest (ROI)that relates to extraction of at least one target nerve fiber in thesubject; determining, based on the anatomical image, the at least oneROI in the diffusion image; and extracting, from the diffusion image, atleast one of the at least one target nerve fiber based on the at leastone ROI.

In some embodiments, the determining, based on the anatomical image, theat least one ROI in the diffusion image may include determining, in theanatomical image, at least one reference region corresponding to the atleast one ROI; and determining, based on the anatomical image and the atleast one reference region, the at least one ROI in the diffusion image.

In some embodiments, the method of claim 2, wherein the determining,based on the anatomical image and the at least one reference region, theat least one ROI in the diffusion image may include determiningregistration information between the anatomical image and the diffusionimage by registering the anatomical image with the diffusion image; anddetermining, in the diffusion image, the at least one ROI based on theregistration information, the anatomical image, and the at least onereference region.

In some embodiments, the determining, in the anatomical image, at leastone reference region corresponding to the at least one ROI may includeobtaining at least one predetermined ROI mask; and determining, in theanatomical image, the at least one reference region based on the atleast one predetermined ROI mask.

In some embodiments, the determining, in the anatomical image, at leastone reference region corresponding to the at least one ROI may includeobtaining a trained extraction model; and determining, in the anatomicalimage, at least one reference region using the trained extraction model.

In some embodiments, the trained extraction model is trained by aprocess including obtaining a preliminary extraction model; obtaining aplurality of training datasets, each of the plurality of trainingdatasets including a historical anatomical image and at least onehistorical reference region identified in the historical anatomicalimage; and training the preliminary extraction model using the pluralityof training datasets to obtain the trained extraction model.

In some embodiments, the determining, in the anatomical image, at leastone reference region corresponding to the at least one ROI may includeobtaining, based on at least one of a default setting or a user input,information related to the at least one target nerve fiber; anddetermining, based on the information related to the at least one targetnerve fiber, the at least one reference region in the anatomical image.

In some embodiments, the extracting, from the diffusion image, at leastone of the at least one target nerve fiber based on the at least one ROImay include identifying, based on the diffusion image, at least onecandidate nerve fiber using a tracking algorithm; and extracting, fromthe diffusion image, the at least one of the at least one target nervefiber selected from the at least one candidate nerve fiber.

In some embodiments, the identifying, based on the diffusion image, atleast one candidate nerve fiber using a tracking algorithm may includedetermining a mask image by excluding one or more background regionsfrom the diffusion image, the one or more background regions beingunrelated to the extraction of the at least one target nerve fiber; andextracting, from the mask image, the at least one candidate nerve fiberusing the tracking algorithm.

In some embodiments, the determining a mask image may includedetermining characteristic data based on the diffusion image; anddetermining the mask image based on the characteristic data.

In some embodiments, the determining a mask image may include obtainingthe mask image by segmenting the diffusion image.

In some embodiments, the at least one ROI may include at least one of afirst ROI or a second ROI. The at least one target nerve fiber may passthrough the first ROI. The at least one target nerve fiber does not passthrough the second ROI.

In some embodiments, the at least one ROI may include the first ROI, andthe extracting, from the diffusion image, at least one of the at leastone target nerve fiber based on the at least one ROI may includedetermining at least a portion of the first ROI as a seed point, andextracting, from the diffusion image and based on the seed point, the atleast one of the at least one target nerve fiber that passes through thefirst ROI using a tracking algorithm.

In some embodiments, the at least one ROI may further include the secondROI, and the extracting, from the diffusion image and based on thediffusion image, the at least one of the at least one target nerve fiberthat passes through the first ROI using a tracking algorithm may includedetermining, in the diffusion image and based on the seed point, atleast one candidate nerve fiber that passes through the first ROI usingthe tracking algorithm; and for each of the at least one candidate nervefiber, determining whether the candidate nerve fiber passes through thesecond ROI; and in response to determining that the candidate nervefiber does not pass through the second ROI, designating the candidatenerve fiber as one of the at least one target nerve fiber.

In some embodiments, the method may further include obtaining areference user input regarding the at least one ROI; and determining,based on the reference user input, whether to update at least a portionof the at least one ROI.

According to another aspect of the present disclosure, a system isprovided. The system may include at least one non-transitory storagemedium including a set of instructions for image-based nerve fiberextraction and at least one processor in communication with the at leastone non-transitory storage medium. When executing the set ofinstructions, the at least one processor is configured to cause thesystem to perform operations including obtaining an anatomical image ofa subject and a diffusion image of the subject, the subject including atleast one region of interest (ROI) that relates to extraction of atleast one target nerve fiber in the subject; determining, based on theanatomical image, the at least one ROI in the diffusion image; andextracting, from the diffusion image, at least one of the at least onetarget nerve fiber based on the at least one ROI.

According to yet another aspect of the present disclosure, anon-transitory computer readable medium is provided. The non-transitorycomputer readable medium may include at least one set of instructions.When executed by at least one processor of a computing device, the atleast one set of instructions may direct the at least one processor toperform operations including obtaining an anatomical image of a subjectand a diffusion image of the subject, the subject including at least oneregion of interest (ROI) that relates to extraction of at least onetarget nerve fiber in the subject; determining, based on the anatomicalimage, the at least one ROI in the diffusion image; and extracting, fromthe diffusion image, at least one of the at least one target nerve fiberbased on the at least one ROI.

Additional features will be set forth in part in the description whichfollows, and in part will become apparent to those skilled in the artupon examination of the following and the accompanying drawings or maybe learned by production or operation of the examples. The features ofthe present disclosure may be realized and attained by practice or useof various aspects of the methodologies, instrumentalities, andcombinations set forth in the detailed examples discussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. The drawings are not to scale. Theseembodiments are non-limiting exemplary embodiments, in which likereference numerals represent similar structures throughout the severalviews of the drawings, and wherein:

FIG. 1 is a schematic diagram illustrating an exemplary imaging systemaccording to some embodiments of the present disclosure;

FIG. 2 is a schematic diagram illustrating an exemplary nerve fiberextraction device according to some embodiments of the presentdisclosure;

FIG. 3 is a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary mobile device according to someembodiments of the present disclosure;

FIG. 4 is a block diagram illustrating an exemplary nerve fiberextraction device according to some embodiments of the presentdisclosure;

FIG. 5 is a flowchart illustrating an exemplary process for target nervefiber extraction according to some embodiments of the presentdisclosure;

FIG. 6 is a flowchart illustrating an exemplary process for extractingnerve fibers related to at least one ROI according to some embodimentsof the present disclosure;

FIG. 7 is a flowchart of illustrating an exemplary process forextracting at least one target nerve fiber;

FIG. 8 is a flowchart illustrating an exemplary process for extractingat least one target nerve fiber according to some embodiments of thepresent disclosure;

FIG. 9 is a flowchart illustrating an exemplary process for extractingat least one target nerve fiber according to some embodiments of thepresent disclosure;

FIG. 10 is a schematic diagram illustrating an exemplary method forextracting at least one target nerve fiber according to some embodimentsof the present disclosure; and

FIG. 11 is a flowchart illustrating an exemplary process for extractingat least one target nerve fiber according to some embodiments of thepresent disclosure.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of examples in order to provide a thorough understanding ofthe relevant disclosure. However, it should be apparent to those skilledin the art that the present disclosure may be practiced without suchdetails. In other instances, well-known methods, procedures, systems,components, and/or circuitry have been described at a relativelyhigh-level, without detail, in order to avoid unnecessarily obscuringaspects of the present disclosure. Various modifications to thedisclosed embodiments will be readily apparent to those skilled in theart, and the general principles defined herein may be applied to otherembodiments and applications without departing from the spirit and scopeof the present disclosure. Thus, the present disclosure is not limitedto the embodiments shown, but to be accorded the widest scope consistentwith the claims.

The terminology used herein is for the purpose of describing particularexample embodiments only and is not intended to be limiting. As usedherein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprise,”“comprises,” and/or “comprising,” “include,” “includes,” and/or“including,” when used in this specification, specify the presence ofstated features, integers, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof.

It will be understood that the term “system,” “engine,” “unit,”“module,” and/or “block” used herein are one method to distinguishdifferent components, elements, parts, section or assembly of differentlevel in ascending order. However, the terms may be displaced by anotherexpression if they achieve the same purpose. The term “image” in thepresent disclosure is used to collectively refer to image data (e.g.,scan data, projection data) and/or images of various forms, including atwo-dimensional (2D) image, a three-dimensional (3D) image, afour-dimensional (4D), etc.

Generally, the word “module,” “unit,” or “block,” as used herein, refersto logic embodied in hardware or firmware, or to a collection ofsoftware instructions. A module, a unit, or a block described herein maybe implemented as software and/or hardware and may be stored in any typeof non-transitory computer-readable medium or another storage device. Insome embodiments, a software module/unit/block may be compiled andlinked into an executable program. It will be appreciated that softwaremodules can be callable from other modules/units/blocks or fromthemselves, and/or may be invoked in response to detected events orinterrupts. Software modules/units/blocks configured for execution oncomputing devices (e.g., processor 210 as illustrated in FIG. 2) may beprovided on a computer-readable medium, such as a compact disc, adigital video disc, a flash drive, a magnetic disc, or any othertangible medium, or as a digital download (and can be originally storedin a compressed or installable format that needs installation,decompression, or decryption prior to execution). Such software code maybe stored, partially or fully, on a storage device of the executingcomputing device, for execution by the computing device. Softwareinstructions may be embedded in firmware, such as an EPROM. It will befurther appreciated that hardware modules/units/blocks may be includedin connected logic components, such as gates and flip-flops, and/or canbe included of programmable units, such as programmable gate arrays orprocessors. The modules/units/blocks or computing device functionalitydescribed herein may be implemented as software modules/units/blocks butmay be represented in hardware or firmware. In general, themodules/units/blocks described herein refer to logicalmodules/units/blocks that may be combined with othermodules/units/blocks or divided into sub-modules/sub-units/sub-blocksdespite their physical organization or storage. The description may beapplicable to a system, an engine, or a portion thereof.

It will be understood that when a unit, engine, module or block isreferred to as being “on,” “connected to,” or “coupled to,” anotherunit, engine, module, or block, it may be directly on, connected orcoupled to, or communicate with the other unit, engine, module, orblock, or an intervening unit, engine, module, or block may be present,unless the context clearly indicates otherwise. As used herein, the term“and/or” includes any and all combinations of one or more of theassociated listed items.

These and other features, and characteristics of the present disclosure,as well as the methods of operation and functions of the relatedelements of structure and the combination of parts and economies ofmanufacture, may become more apparent upon consideration of thefollowing description with reference to the accompanying drawings, allof which form a part of this disclosure. It is to be expresslyunderstood, however, that the drawings are for the purpose ofillustration and description only and are not intended to limit thescope of the present disclosure. It is understood that the drawings arenot to scale.

FIG. 1 is a schematic diagram illustrating an exemplary system 100 forimage-based nerve fiber extraction according to some embodiments of thepresent disclosure. As shown, the system 100 may include an imagingdevice 110, a network 120, one or more terminals 130, a processingdevice 140, and a storage device 150. In some embodiments, the imagingdevice 110, the terminal(s) 130, the processing device 140, and/or thestorage device 150 may be connected to and/or communicate with eachother via a wireless connection (e.g., the network 120), a wiredconnection, or a combination thereof. The connection between thecomponents of the system 100 may be variable. Merely by way of example,the imaging device 110 may be connected to the processing device 140through the network 120, as illustrated in FIG. 1. As another example,the imaging device 110 may be connected to the processing device 140directly. As a further example, the storage device 150 may be connectedto the processing device 140 through the network 120, as illustrated inFIG. 1, or connected to the processing device 140 directly. As still afurther example, a terminal 130 may be connected to the processingdevice 140 through the network 120, as illustrated in FIG. 1, orconnected to the processing device 140 directly.

The imaging device 110 may generate or provide image data via scanning asubject (e.g., a patient) disposed on a scanning table of the imagingdevice 110. In some embodiments, the imaging device 110 may include asingle-modality scanner and/or multi-modality scanner. Thesingle-modality scanner may include, for example, a computed tomography(CT) scanner, a magnetic resonance imaging (MRI) scanner, anultrasonography scanner, a positron emission tomography (PET) scanner,etc. The multi-modality scanner may include a single photon emissioncomputed tomography-computed tomography (SPECT-CT) scanner, a positronemission tomography-computed tomography (PET-CT) scanner, a computedtomography-ultra-sonic (CT-US) scanner, a digital subtractionangiography-computed tomography (DSA-CT) scanner, or the like, or acombination thereof. In some embodiments, the image data may includeprojection data, images relating to the subject, etc. The projectiondata may be raw data generated by the imaging device 110 by scanning thesubject or data generated by a forward projection on an image relatingto the subject. In some embodiments, the subject may include a body, asubstance, an object, or the like, or a combination thereof. In someembodiments, the subject may include a specific portion of a body, suchas a head, a thorax, an abdomen, or the like, or a combination thereof.In some embodiments, the subject may include a specific organ or regionof interest, such as an esophagus, a trachea, a bronchus, a stomach, agallbladder, a small intestine, a colon, a bladder, a ureter, a uterus,a fallopian tube, etc.

In some embodiments, the imaging device 110 may include a gantry 111, adetector 112, a detecting region 113, a scanning table 114, and aradioactive scanning source 115. The gantry 111 may support the detector112 and the radioactive scanning source 115. A subject may be placed onthe scanning table 114 to be scanned. The radioactive scanning source115 may emit radioactive rays to the subject. The radiation may includea particle ray, a photon ray, or the like, or a combination thereof. Insome embodiments, the radiation may include a plurality of radiationparticles (e.g., neutrons, protons, electrons, p-mesons, heavy ions), aplurality of radiation photons (e.g., X-ray, a y-ray, ultraviolet,laser), or the like, or a combination thereof. The detector 112 maydetect radiations and/or radiation events (e.g., gamma photons) emittedfrom the detecting region 113. In some embodiments, the detector 112 mayinclude a plurality of detector units. The detector units may include ascintillation detector (e.g., a cesium iodide detector) or a gasdetector. The detector unit may be a single-row detector or a multi-rowsdetector.

In some embodiments, the imaging device 110 may be integrated with oneor more other devices that may facilitate the scanning of the subject,such as an image-recording device. The image-recording device may beconfigured to take various types of images related to the subject. Forexample, the image-recording device may be a two-dimensional (2D) camerathat takes pictures of the exterior or outline of the subject. Asanother example, the image-recording device may be a 3D scanner (e.g., alaser scanner, an infrared scanner, a 3D CMOS sensor) that records thespatial representation of the subject.

The network 120 may include any suitable network that can facilitateexchange of information and/or data for the system 100. In someembodiments, one or more components of the system 100 (e.g., the imagingdevice 110, the processing device 140, the storage device 150, theterminal(s) 130) may communicate information and/or data with one ormore other components of the system 100 via the network 120. Forexample, the processing device 140 may obtain image data from theimaging device 110 via the network 120. As another example, theprocessing device 140 may obtain user instruction(s) from theterminal(s) 130 via the network 120. The network 120 may be or include apublic network (e.g., the Internet), a private network (e.g., a localarea network (LAN)), a wired network, a wireless network (e.g., an802.11 network, a Wi-Fi network), a frame relay network, a virtualprivate network (VPN), a satellite network, a telephone network,routers, hubs, switches, server computers, and/or any combinationthereof. For example, the network 120 may include a cable network, awireline network, a fiber-optic network, a telecommunications network,an intranet, a wireless local area network (WLAN), a metropolitan areanetwork (MAN), a public telephone switched network (PSTN), a Bluetooth™network, a ZigBee™ network, a near field communication (NFC) network, orthe like, or any combination thereof. In some embodiments, the network120 may include one or more network access points. For example, thenetwork 120 may include wired and/or wireless network access points suchas base stations and/or internet exchange points through which one ormore components of the system 100 may be connected to the network 120 toexchange data and/or information.

The terminal(s) 130 may be connected to and/or communicate with theimaging device 110, the processing device 140, and/or the storage device150. For example, the terminal(s) 130 may obtain a processed image fromthe processing device 140. As another example, the terminal(s) 130 mayobtain image data acquired via the imaging device 110 and transmit theimage data to the processing device 140 to be processed. In someembodiments, the terminal(s) 130 may include a mobile device 131, atablet computer 132, a laptop computer 133, or the like, or anycombination thereof. For example, the mobile device 131 may include amobile phone, a personal digital assistant (PDA), a gaming device, anavigation device, a point of sale (POS) device, a laptop, a tabletcomputer, a desktop, or the like, or any combination thereof. In someembodiments, the terminal(s) 130 may include an input device, an outputdevice, etc. The input device may include alphanumeric and other keysthat may be input via a keyboard, a touch screen (for example, withhaptics or tactile feedback), a speech input, an eye tracking input, abrain monitoring system, or any other comparable input mechanism. Theinput information received through the input device may be transmittedto the processing device 140 via, for example, a bus, for furtherprocessing. Other types of the input device may include a cursor controldevice, such as a mouse, a trackball, or cursor direction keys, etc. Theoutput device may include a display, a speaker, a printer, or the like,or a combination thereof. In some embodiments, the terminal(s) 130 maybe part of the processing device 140.

The processing device 140 may process data and/or information obtainedfrom the imaging device 110, the storage device 150, the terminal(s)130, or other components of the system 100. For example, the processingdevice 140 may reconstruct an image based on scan data generated by theimaging device 110. As another example, the processing device 140 may bedirected to extract one or more target nerve fibers from a diffusionimage. As yet another example, the processing device 140 may be directedto determine at least one ROI in the diffusion image that is related tothe extraction of the one or more target nerve fibers. In someembodiments, the processing device 140 may be a single server or aserver group. The server group may be centralized or distributed. Insome embodiments, the processing device 140 may be local to or remotefrom the system 100. For example, the processing device 140 may accessinformation and/or data from the imaging device 110, the storage device150, and/or the terminal(s) 130 via the network 120. As another example,the processing device 140 may be directly connected to the imagingdevice 110, the terminal(s) 130, and/or the storage device 150 to accessinformation and/or data. In some embodiments, the processing device 140may be implemented on a cloud platform. For example, the cloud platformmay include a private cloud, a public cloud, a hybrid cloud, a communitycloud, a distributed cloud, an inter-cloud, a multi-cloud, or the like,or a combination thereof. In some embodiments, the processing device 140may be implemented by a computing device 200 having one or morecomponents as described in connection with FIG. 2.

The storage device 150 may store data, instructions, and/or any otherinformation. In some embodiments, the storage device 150 may store dataobtained from the processing device 140, the terminal(s) 130, and/or thestorage device 150. In some embodiments, the storage device 150 maystore data and/or instructions that the processing device 140 mayexecute or use to perform exemplary methods described in the presentdisclosure. In some embodiments, the storage device 150 may include amass storage device, a removable storage device, a volatileread-and-write memory, a read-only memory (ROM), or the like, or anycombination thereof. Exemplary mass storage may include a magnetic disk,an optical disk, a solid-state drive, etc. Exemplary removable storagemay include a flash drive, a floppy disk, an optical disk, a memorycard, a zip disk, a magnetic tape, etc. Exemplary volatileread-and-write memory may include a random access memory (RAM).Exemplary RAM may include a dynamic RAM (DRAM), a double date ratesynchronous dynamic RAM (DDR SDRAM), a static RAM (SRAM), a thyristorRAM (T-RAM), and a zero-capacitor RAM (Z-RAM), etc. Exemplary ROM mayinclude a mask ROM (MROM), a programmable ROM (PROM), an erasableprogrammable ROM (EPROM), an electrically erasable programmable ROM(EEPROM), a compact disk ROM (CD-ROM), and a digital versatile disk ROM,etc. In some embodiments, the storage device 150 may be implemented on acloud platform as described elsewhere in the disclosure.

In some embodiments, the storage device 150 may be connected to thenetwork 120 to communicate with one or more other components of thesystem 100 (e.g., the processing device 140, the terminal(s) 130). Oneor more components of the system 100 may access the data or instructionsstored in the storage device 150 via the network 120. In someembodiments, the storage device 150 may be part of the processing device140.

This description is intended to be illustrative, and not to limit thescope of the present disclosure. Many alternatives, modifications, andvariations will be apparent to those skilled in the art. The features,structures, methods, and other characteristics of the exemplaryembodiments described herein may be combined in various ways to obtainadditional and/or alternative exemplary embodiments. For example, thestorage device 150 may be a data storage including cloud computingplatforms, such as public cloud, private cloud, community, and hybridclouds, etc. However, those variations and modifications do not departthe scope of the present disclosure.

FIG. 2 is a schematic diagram illustrating an exemplary nerve fiberextraction device according to some embodiments of the presentdisclosure. The nerve fiber extraction device 200 shown in FIG. 2 isprovided for illustration purposes, and should not bring any limitationto the function and application scope of the embodiments of the presentdisclosure.

As shown in FIG. 2, the nerve fiber extraction device 200 is representedin the form of a computing device. The components of the nerve fiberextraction device 200 in the ROI may include, but are not limited to:one or more processors or processing units 210, a storage 220, and a bus230 connecting different system components (including the storage 220and the processing unit 210).

The bus 230 represents one or more of several types of bus structures,including a memory bus, a memory controller, a peripheral bus, agraphics acceleration port, a processor, or a local bus using any busstructure among multiple bus structures. For example, thesearchitectures include but are not limited to industry standardarchitecture (ISA) bus, microchannel architecture (MAC) bus, enhancedISA bus, Video Electronics Standards Association (VESA) local bus, andperipheral component interconnection (PCI) bus.

The nerve fiber extraction device 200 of the ROI typically includes avariety of computer system readable media. These media may be anyavailable media that can be accessed by the nerve fiber extractiondevice 200, including volatile and nonvolatile media, removable andnon-removable media.

The storage 220 may include computer system readable media in the formof volatile memory, such as random access memory (RAM) 221 and/or cachememory 222. The nerve fiber extraction device 200 of the ROI may furtherinclude other removable/non-removable, volatile/nonvolatile computersystem storage media. For example only, the storage system 223 can beused to read and write non-removable, non-volatile magnetic media (notshown in FIG. 8, usually referred to as a “hard drive”). Although notshown in FIG. 8, a disk drive for reading and writing to removable/non-volatile disks (such as “floppy disks”), as well as removablenon-volatile disks (such as CD-ROM, DVD- ROM or other optical media)read and write optical disc drives. In these cases, each drive can beconnected to the bus 230 through one or more data media interfaces. Thememory 220 may include at least one program product having a set ofprogram modules 225(for example, the obtaining module 410, theextracting module 420 and the tracking module 430 of the nerve fiberextraction device 400), which are configured to execute a nerve fiberextraction process described in the present disclosure.

In some embodiments, a program/utility tool 224 including the set ofprogram modules 225 can be stored in the memory 220. For example, suchprogram modules 225 may include but not limited to an operating system,one or more application programs, other program modules, program data,etc. Each of these examples or a certain combination may include theimplementation of a network environment. The program module 225generally executes the functions and/or methods in the describedembodiments of the present disclosure.

The nerve fiber extraction device 200 can also communicate with one ormore external devices 280 (such as keyboards, pointing devices, adisplay 285, etc.), and can also communicate with one or more devicesthat allow user interactions (e.g., a terminal device). The extractiondevice 200 may also communicate with one or more devices (such as anetwork card, modem, etc.) that enables the nerve fiber extractiondevice 200 to communicate with one or more other computing devices. Suchcommunication can be performed through an input/output (I/O) interface290. In addition, the nerve fiber extraction device 200 may alsocommunicate with one or more networks (for example, a local area network(LAN), a wide area network (WAN), and/or a public network, such as theInternet) through a network adapter 295. As shown in the figure, thenetwork adapter 295 may communicate with other components of the nervefiber extraction device 200 through the bus 230. It should be understoodthat although not shown in the figure, other hardware and/or softwaremodules can be used in conjunction with the nerve fiber extractiondevice 200, including but not limited to: microcomputers, devicedrivers, redundant processing units, external disk drive arrays, RAIDsystems, tape drives, and data backup storage systems, etc.

The processing unit 210 may implement various functions by runningprograms stored in the storage 220, such as implementing a method forextracting nerve fibers related to at least one ROI provided by someembodiments of the present disclosure. The method may include obtainingan anatomical image and a diffusion image (e.g., a diffusion tensorimage) of a current detection part, where the diffusion image includesthe diffusion data of the current detection part, and the anatomicalimage contains the anatomical data of the current detection part;extracting at least one ROI of the diffusion image based on theanatomical data, the diffusion data, and the pre-stored ROI mask data ofthe current detection part; and tracking a target nerve fiber in thediffusion image using a preset tracking algorithm based on the at leastone ROI.

The processing unit 210 may execute various functions by runningprograms stored in the storage 220, such as implementing a method forextracting nerve fibers based on at least one ROI provided by someembodiments of the present disclosure.

FIG. 3 is a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary mobile device 300 on which theterminals 130 may be implemented according to some embodiments of thepresent disclosure. As illustrated in FIG. 3, the mobile device 300 mayinclude a communication platform 310, a display 320, a graphicprocessing unit (GPU) 330, a central processing unit (CPU) 340, an I/O350, a memory 360, and a storage 390. In some embodiments, any othersuitable component, including but not limited to a system bus or acontroller (not shown), may also be included in the mobile device 300.In some embodiments, a mobile operating system 370 (e.g., iOS™,Android™, Windows Phone™) and one or more applications 380 may be loadedinto the memory 360 from the storage 390 in order to be executed by theCPU 340. The applications 380 may include a browser or any othersuitable mobile apps for receiving and rendering information relating toimage processing or other information from the processing device 140.User interactions with the information stream may be achieved via theI/O 350 and provided to the processing device 140 and/or othercomponents of the system 100 via the network 120.

To implement various modules, units, and their functionalities describedin the present disclosure, computer hardware platforms may be used asthe hardware platform(s) for one or more of the elements describedherein. A computer with user interface elements may be used to implementa personal computer (PC) or any other type of work station or terminaldevice. A computer may also act as a server if appropriately programmed.

FIG. 4 is a block diagram illustrating an exemplary nerve fiberextraction device 400 according to some embodiments of the presentdisclosure. In some embodiments, the nerve fiber extraction device 400may be implemented on a processing device (e.g., the processing device140). As illustrated in FIG. 4, the nerve fiber extraction device 400may include an obtaining module 410, an extracting module 420, and atracking module 430. The modules may be hardware circuits of all or partof the nerve fiber extraction device 400. The modules may also beimplemented as an application or set of instructions read and executedby the nerve fiber extraction device 400. Further, the modules may beany combination of the hardware circuits and theapplication/instructions. For example, the modules may be the part ofthe nerve fiber extraction device 400 when the nerve fiber extractiondevice 400 is executing the application/set of instructions.

The obtaining module 410 may acquire data related to the system 100. Forinstance, the obtaining module 410 may obtain an anatomical image of asubject and a diffusion image of the subject. The subject may include atleast one region of interest (ROI) that relates to extraction of atleast one target nerve fiber in the subject. For example, the subjectmay include a specific portion of a patient, such as the head, theabdomen, the spine, the heart, or the like, or a combination thereof, ofa patient. In some embodiments, the at least one ROI may include an ROIthrough which a target nerve fiber passes. As used herein, an ROIthrough which a target nerve fiber passes is also referred to as a firstROI. Additionally or alternatively, the at least one ROI may include anROI though which no target nerve fiber pass. As used herein, an ROIthrough which no target nerve fiber passes is also referred to as asecond ROI; that is a second ROI is void of a target nerve fiber.

The extracting module 420 may extract at least one ROI in the diffusionimage based on the anatomical image and other data obtained by theobtaining module 410. In some embodiments, the processing device 140 maydetermine at least one reference region corresponding to the at leastone ROI in the anatomical image. For example, the processing device 140may determine the at least one reference region in the anatomical imageusing a trained extraction model or a predetermined template (alsoreferred to as an “ROI mask”). The trained extraction model and thepredetermined ROI mask may correspond to the at least one target nervefiber to be extracted. For instance, the processing device 140 mayretrieve the trained extraction model or the predetermined ROI mask fromthe storage device according to the information related to the at leastone target nerve fiber.

The tracking module 430 may track at least one target nerve fibers basedon the at least one ROI determined by the extracting module 420. Forinstance, the at least one ROI may include a first ROI and a second ROI.The tracking module 430 may track all the nerve fibers in the diffusionimage using a tracking algorithm, and designate all the tracked nervefibers as the candidate nerve fibers. The tracking module 430 mayfurther select at least one candidate nerve fiber that passes throughthe first ROI but does not pass through the second ROI as at least onetarget nerve fiber. Alternatively, the tracking module 430 may determineat least one candidate nerve fiber that passes through the first ROIbased on a regional growth method using the seed point. For each of theat least one candidate nerve fiber, the tracking module 430 maydetermine whether the candidate nerve fiber passes through the secondROI; and in response to determining that the candidate nerve fiber doesnot pass through the second ROI, the tracking module 430 may designatethe candidate nerve fiber as one of the at least one target nerve fiber.For instance, the seed point may be determined based on a first ROIthrough which a target nerve fiber may pass. The seed point may includea point in the first ROI, a region in the first ROI, the entire firstROI, etc. More details regarding the determination of the at least onecandidate nerve fiber may be found elsewhere in the present disclosure,for example, in FIG. 9, FIG. 10, and the description thereof.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations or modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. In someembodiments, any module mentioned above may be divided into two or moreunits. In some embodiments, the nerve fiber extraction device 400 mayinclude one or more additional modules. For example, the nerve fiberextraction device 400 may further include a control module configured togenerate control signals for one or more components in the system 100.In some embodiments, one or more modules of the nerve fiber extractiondevice 400 described above may be omitted.

FIG. 5 is a flowchart illustrating an exemplary process for target nervefiber extraction according to some embodiments of the presentdisclosure. At least a portion of process 500 may be implemented on thenerve fiber extraction device 200 as illustrated in FIG. 2 or the nervefiber extraction device 400 as illustrated in FIG. 4. In someembodiments, one or more operations of the process 500 may beimplemented in the system 100 as illustrated in FIG. 1. In someembodiments, one or more operations in the process 500 may be stored inthe storage device 150 and/or the storage (e.g., the storage 220, etc.)as a form of instructions, and invoked and/or executed by the system100, or a portion thereof (e.g., the processing device 140). In someembodiments, the instructions may be transmitted in a form of electroniccurrent or electrical signals.

In 502, the processing device 140 (e.g., the obtaining module 410) mayobtain an anatomical image of a subject and a diffusion image of thesubject. The subject may include at least one region of interest (ROI)that relates to extraction of at least one target nerve fiber in thesubject. For example, the subject may include a specific portion of apatient, such as the head, the abdomen, the spine, the heart, or thelike, or a combination thereof, of a patient. Merely by way of example,the subject may include the brain, and the at least one target nervefiber may include a corticospinal tract, an optic tract, an upperlongitudinal tract, a lower longitudinal tract, a cingulate, a corpuscallosum, or the like, or any combination thereof.

In some embodiments, the at least one ROI may include an ROI throughwhich a target nerve fiber passes. As used herein, an ROI through whicha target nerve fiber passes is also referred to as a first ROI.Additionally or alternatively, the at least one ROI may include an ROIthough which no target nerve fiber pass. As used herein, an ROI throughwhich no target nerve fiber passes is also referred to as a second ROI;that is a second ROI is void of a target nerve fiber. For example, theat least one ROI may include one or more first ROIs, and a target nervefiber to be extracted may pass through each of the one or more firstROIs. As another example, the at least one ROI may include one or moresecond ROIs, a target nerve fiber to be extracted does not pass throughany of the one or more second ROIs. As yet another example, a targetnerve fiber may pass through one or more first ROIs but not any secondROI. Additionally or alternatively, the at least one ROI may include aselection group of ROIs. A target nerve fiber may pass through at leastsome (e.g., one) of the selection group of ROIs.

As used herein, the term “anatomical image” refers to an image showingan anatomical structure of the subject, and/or information thereof. Insome embodiments, the anatomical image may be acquired using a medicalimaging device (e.g., the imaging device 110) by a scan of the subject.The imaging device may include a CT device, an X-ray imaging device, anMRI device, a PET device, an ultrasound imaging device, a DR device,etc.

As used herein, the term “diffusion image” refers to an image acquiredusing a diffusion-weighted magnetic resonance imaging technique. Thediffusion-weighted magnetic resonance imaging technique utilizes thediffusion of water molecules to generate contrast in an MR image. Watermolecule diffusion patterns can reveal microscopic details about tissuearchitecture. Merely by way of example, the diffusion-weighted magneticresonance imaging technique may include a diffusion tensor imaging (DTI)technique, which is commonly used in image-based extraction of one ormore nerve fibers in the brain. More details regarding the DTI techniquemay be found elsewhere in the present disclosure. See, for example, thedescription of operation 602. In some embodiments, the anatomical imageand the diffusion image obtained in operation 502 may relate to a sameportion of a patient, such as the head of the patient. Merely by way ofexample, the anatomical image may be a normal MR image of the subject,and the diffusion image may be a diffusion tensor image of the subject.As yet another example, the diffusion image may be acquired using adiffusion kurtosis imaging (DKI) technique that extends conventional DTIby estimating the kurtosis of the water diffusion probabilitydistribution function.

In 504, the processing device 140 (e.g., the extracting module 420) maydetermine at least one ROI in the diffusion image based on theanatomical image. In some embodiments, the at least one ROI includes oneor more first ROIs through which at least one target fiber passes.Additionally or alternatively, the at least one ROI may include one ormore second ROIs through which no target fiber pass.

In some embodiments, the processing device 140 may obtain informationrelated to the at least one target nerve fiber. For example, theinformation related to the at least one target nerve fiber may include aname, a classification, a length, a diameter, or the like, or anycombination thereof, of the at least one target nerve fiber. Suchinformation may be obtained from a default setting stored in a storagedevice (e.g., the storage device 150). Alternatively, a user may inputat least some of the information related to the at least one targetnerve fiber via a terminal device. For example, the user may input thename(s) of the at least one target fiber through an input device (e.g.,a keyboard) of the terminal device. As another example, the terminaldevice may display names of a plurality of nerve fibers. The user mayspecify the at least one target nerve fiber by selecting at least one ofthe plurality of nerve fibers via the input device (e.g., a keyboardand/or a mouse).

In some embodiments, the processing device 140 may determine at leastone reference region corresponding to the at least one ROI in theanatomical image. For example, the processing device 140 may determinethe at least one reference region in the anatomical image using atrained extraction model or a predetermined template (also referred toas an “ROI mask”). The trained extraction model and the predeterminedROI mask may correspond to the at least one target nerve fiber to beextracted. For instance, the processing device 140 may retrieve thetrained extraction model or the predetermined ROI mask from the storagedevice according to the information related to the at least one targetnerve fiber.

The processing device 140 may further determine the at least one ROI inthe diffusion image based on the at least one reference region andregistration information between the anatomical image and the diffusionimage.

To obtain the registration information between the anatomical image andthe diffusion image, the processing device 140 may register theanatomical image and the diffusion image using an image registrationalgorithm. For example, the image registration algorithm may include anintensity-based algorithm, a feature-based algorithm, a coordinatetransformation algorithm, a spatial domain algorithm, a frequency domainalgorithm, or the like, or any combination thereof. The registrationinformation may include, for example, a registration matrix, a mappingrelationship between pixels (or voxels) of the anatomical image andpixels (or voxels) of the diffusion image. More details regarding theregistration of the anatomical image and the diffusion image may befound elsewhere in the present disclosure, for example, in FIGS. 6 and7.

In some embodiments, the processing device 140 may transmit informationof the at least one ROI (or referred to as ROI information) to aterminal device for display. Merely by way of example, the ROIinformation may be in the form of an image, e.g., a portion of thediffusion image that includes primarily the at least one ROI. As usedherein, “primarily” indicates that at least a certain percentage (e.g.,at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, atleast 95%) of the portion includes or is occupied by a representation ofthe at least one ROI. The ROI information may include a representationof the at least one ROI indicating at least one feature of the at leastone ROI including, e.g., the shape, the position, the size, or the like,or a combination thereof, of each of the at least one ROI. A user mayview the ROI information on a display of the terminal device and checkwhether the ROI information needs to be updated. The processing device140 may obtain a reference user input regarding the ROI information, anddetermines whether to update at least a portion of one or more of the atleast one ROI. If the user decides that at least a portion of the ROIinformation needs to be updated, the user may, for example, adjust theshape, the position, and/or the size of any one of the at least one ROI.If the user decides that none of the ROI information needs to beupdated, the user may input an instruction to the terminal device tocause the processing device 140 proceed to operation 506.

In 506, the processing device 140 (e.g., the tracking module 430) mayextract at least one of the at least one target nerve fiber from thediffusion image based on the at least one ROI. In some embodiments, theprocessing device 140 may determine a mask image in the diffusion imageand extract the at least one of the at least one target nerve fiber fromthe mask image. As used herein, the mask image determined in thediffusion image refers to a portion of the diffusion image that excludesone or more background regions that are unrelated to the extraction ofthe at least one target nerve fiber. For instance, the one or morebackground regions may include a region where nerve fibers in thesubject (or the at least one target nerve fiber) are unlikely to pass,or the like, or any combination thereof. More descriptions regarding themask image and the ROI extraction from the mask image may be foundelsewhere in the present disclosure, for example, in FIG. 9 and thedescription thereof.

In some embodiments, the processing device 140 may select one or moretarget nerve fibers from at least one candidate nerve fiber based on theat least one ROI. For instance, the at least one ROI may include a firstROI and a second ROI. The processing device 140 may track all the nervefibers in the diffusion image using a tracking algorithm, and designateall the tracked nerve fibers as the candidate nerve fibers. Theprocessing device 140 may further select at least one candidate nervefiber that passes through the first ROI but does not pass through thesecond ROI as at least one target nerve fiber. Alternatively, theprocessing device 140 may determine at least one candidate nerve fiberthat passes through the first ROI based on a regional growth methodusing the seed point. For each of the at least one candidate nervefiber, the processing device 140 may determine whether the candidatenerve fiber passes through the second ROI; and in response todetermining that the candidate nerve fiber does not pass through thesecond ROI, the processing device 140 may designate the candidate nervefiber as one of the at least one target nerve fiber. For instance, theseed point may be determined based on a first ROI through which a targetnerve fiber may pass. The seed point may include a point in the firstROI, a region in the first ROI, the entire first ROI, etc. More detailsregarding the determination of the at least one candidate nerve fibermay be found elsewhere in the present disclosure, for example, in FIG.9, FIG. 10, and the description thereof.

It should be noted that the above description regarding the process 500is merely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations or modifications may be madeunder the teachings of the present disclosure. However, those variationsand modifications do not depart from the scope of the presentdisclosure.

FIG. 6 is a flowchart illustrating an exemplary process for extractingnerve fibers related to at least one ROI according to some embodimentsof the present disclosure. Process 600 is suitable for automaticallyextracting at least one ROI and performing nerve fiber tracking based onthe automatically extracted ROI. At least a portion of process 600 maybe implemented on the nerve fiber extraction device 200 as illustratedin FIG. 2 or the nerve fiber extraction device 400 as illustrated inFIG. 4. In some embodiments, one or more operations of the process 600may be implemented in the system 100 as illustrated in FIG. 1. In someembodiments, one or more operations in the process 600 may be stored inthe storage device 150 and/or the storage (e.g., the storage 220, etc.)as a form of instructions, and invoked and/or executed by the system100, or a portion thereof (e.g., the processing device 140). In someembodiments, the instructions may be transmitted in a form of electroniccurrent or electrical signals).

In 602, an anatomical image and a diffusion image of a current detectionpart may be obtained. In some embodiments, operation 602 may beperformed by the processing device 140, for example, the extractingmodule 420.

The current detection part, also referred to as a subject, may include,for example, the head, the chest, etc., of a patient. The subject mayalso be referred to as a current detection part. For illustrationpurposes and not intended to limiting the scope of the presentdisclosure, the following description is provided with reference to thehead of a patient as the current detection part.

The diffusion image may include, for example, a DWI image, a DTI image,a DKI image, or the like. The diffusion tensor image is a kind ofmagnetic resonance image obtained by using the diffusion tensor imagingtechnology. The diffusion tensor image may utilize the difference indiffusion characteristics of water molecules in the brain tissuestructure to obtain an image of the brain. Therefore, the diffusiontensor image can present the distribution of nerve fibers through thediffusion of water molecules. It should be noted that the process 600,700, 800, 900, 1000, and 1100 may also be implemented to extract atleast one target nerve fiber from various forms of diffusion images,such as a DWI image or a DTI image.

The diffusion image includes diffusion data of the current detectionpart. The anatomical image includes anatomical data of the currentdetection part.

In some embodiments, the diffusion image may include multiple sets ofdiffusion data each in the form of a matrix. For example, the diffusiondata may include a DWI image B₀ without any gradient, a firstgradient-applied DWI image D₁, and a second gradient-applied DWI imageD₂, . . . , and an nth gradient-applied DWI image D_(n), where n is aninteger. For instance, n is an integer no less than 6.

In 604, at least one ROI of the diffusion image may be extracted basedon the anatomical data, the diffusion data, and the pre-stored ROI maskdata (also referred to as the “predetermined ROI mask” or “predeterminedROI mask data”) of the current detection part. In some embodiments,operation 604 may be performed by the extracting module 420.

The ROI mask data of the current detection part may be standard maskdata related to the current detection part. For instance, a user mayobtain a historical anatomical image of the current detection part of asample patient. The sample patient may be the current patient or adifferent patient. The user may manually determine one or more ROIs inthe historical anatomical image to generate the ROI mask data.

In some embodiments, a mapping relationship or a mapping matrix amongthe anatomical data, the diffusion data, and the ROI mask data can beestablished to obtain a relationship between the diffusion data and theROI mask data. At least one ROI of the diffusion image may be furtherdetermined based on the relationship and the ROI mask data.

In some embodiments, if the automatically extracted ROI does not meetthe needs of the user, the processing device 140 may obtain one or moreediting instructions for the ROI of the diffusion image from the user.The ROI of the diffusion image may be updated according to the editinginstructions to obtain re-extracted ROI of the diffusion tensor untilthe re-extracted ROI meets the requirements.

In 606, a target nerve fiber in the diffusion image may be tracked usinga preset tracking algorithm based on the at least one ROI. In someembodiments, operation 606 may be performed by the tracking module 430.

In some embodiments, the preset tracking algorithm may include at leastone of the Fiber Assignment Continuous Tracking (FACT) algorithm or theTensorLine algorithm. For instance, according to the FACT algorithm fortracking the nerve fibers of interest (also referred to as the targetnerve fibers), the tracking may start from the center point of a seedvoxel (or pixel), extend along a main feature direction of the diffusiontensor until an adjacent pixel or voxel is reached, then change thetracking direction into the main diffusion direction of the newlyincreased speed, and proceed according to this process until a pixel orvoxel satisfying an end condition is reached. Therefore, after at leastone ROI of the diffusion image is extracted, a pixel or voxel may beselected for each of the at least one ROI as a seed point. A growthoperation may start from each seed point, combined with the diffusioncharacteristics of water molecules in the brain tissue, until the growthof each nerve fiber of interest is completed. In this way, at least onetarget nerve fiber in the at least one ROI can be determined.

In some embodiments, the target nerve fibers may include at least one ofthe corticospinal tract, the optic tract, the upper longitudinal tract,the lower longitudinal tract, the cingulate, the corpus callosum, or thelike, or any combination thereof.

By automating the process of target nerve fiber extraction may improvethe tracking efficiency and/or accuracy, reduce cross-user variations,compared to manual extraction according to conventional methods.

It should be noted that the above description regarding the process 600is merely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations or modifications may be madeunder the teachings of the present disclosure. However, those variationsand modifications do not depart from the scope of the presentdisclosure.

FIG. 7 is a flowchart illustrating an exemplary process for extractingat least one target nerve fiber. At least a portion of process 700 maybe implemented on the nerve fiber extraction device 200 as illustratedin FIG. 2 or the nerve fiber extraction device 400 as illustrated inFIG. 4. In some embodiments, one or more operations of the process 700may be implemented in the system 100 as illustrated in FIG. 1. In someembodiments, one or more operations in the process 700 may be stored inthe storage device 150 and/or the storage (e.g., the storage 220, etc.)as a form of instructions, and invoked and/or executed by the system100, or a portion thereof (e.g., the processing device 140). In someembodiments, the instructions may be transmitted in a form of electroniccurrent or electrical signals.

In 702, an anatomical image and a diffusion image of a current detectionpart may be obtained.

In 704, the diffusion data may be registered with the anatomical data toobtain a registration matrix between the diffusion data and theanatomical data.

Optionally, the registration of the diffusion data and the anatomicaldata may be rigid registration or non-rigid registration. For example,the B0 data of the diffusion data may be registered with the anatomicaldata. Alternatively, the Di (1<=i<=n) data of the diffusion data may beregistered with the anatomical data. The diffusion data used for theregistration is not specifically limited by the present disclosure.

In 706, the anatomical data may be registered with the ROI mask data ofthe current detection part to obtain a first mapping relationshipbetween the anatomical data and the ROI mask data.

Optionally, the registration of the diffusion data and the ROI mask dataof the current detection part may be rigid registration or non-rigidregistration.

In 708, a second mapping relationship between the ROI mask data of thecurrent detection part and the diffusion data may be determined based onthe registration matrix and the first mapping relationship.

It is understandable that the registration matrix may include thecorresponding relationship between the diffusion data and the anatomicaldata. The first mapping relationship may include the correspondingrelationship between the anatomical data and the ROI mask data of thecurrent detection part. Therefore, the second mapping relationshipbetween the ROI mask data and the diffusion data of the currentdetection position can be obtained based on the matrix and the firstmapping relationship, that is, the corresponding relationship betweenthe ROI mask data and the diffusion data can be obtained.

In 710, at least one ROI of the diffusion image may be determinedaccording to the second mapping relationship. In some embodiments,operations 704-710 may be performed by the extracting module 420.

It is understandable that since the second mapping relationship includesthe corresponding relationship between the ROI mask data of the currentdetection position and the diffusion data, at least one ROI of thediffusion image can be automatically determined according to the secondmapping relationship.

In 712, a preset tracking algorithm may be used to track at least onetarget nerve fiber based on the at least one ROI.

It should be noted that the above description regarding the process 600is merely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations or modifications may be madeunder the teachings of the present disclosure. However, those variationsand modifications do not depart from the scope of the presentdisclosure.

FIG. 8 is a flowchart illustrating an exemplary process for extractingat least one target nerve fiber according to some embodiments of thepresent disclosure. At least a portion of process 800 may be implementedon the nerve fiber extraction device 200 as illustrated in FIG. 2 or thenerve fiber extraction device 400 as illustrated in FIG. 4. In someembodiments, one or more operations of the process 800 may beimplemented in the system 100 as illustrated in FIG. 1. In someembodiments, one or more operations in the process 800 may be stored inthe storage device 150 and/or the storage (e.g., the storage 220, etc.)as a form of instructions, and invoked and/or executed by the system100, or a portion thereof (e.g., the processing device 140). In someembodiments, the instructions may be transmitted in a form of electroniccurrent or electrical signals.

In 802, an anatomical image and a diffusion image of a current detectionpart may be obtained.

In 804, the anatomical data may be inputted into a trained extractionmodel to obtain at least one ROI of the anatomical image.

The trained extraction model is obtained by training a preliminaryextraction model (e.g., a neural network model) using a plurality oftraining datasets. Each of the plurality of training datasets mayinclude a historical anatomical image and at least one historicalreference region identified in the historical anatomical image. It isunderstandable that the historical reference region may be identifiedmanually in the historical anatomical image.

Optionally, the preliminary extraction model may be a deep learningnetwork model or a convolutional neural network model, or the like,which is not limited by the present disclosure.

In 806, the anatomical image may be registered with the diffusion imageto determine at least one ROI of the diffusion image.

Optionally, the registration of the at least one ROI of the anatomicalimage and the diffusion image may be rigid registration or non-rigidregistration.

In 808, a preset tracking algorithm may be used to track the at leastone target nerve fiber based on the at least one ROI.

Optionally, the original neural network can be trained using at leastone area of interest of the standard diffusion image and the standarddiffusion image to obtain the trained extraction model, and then inputthe acquired diffusion image of the current detection part After thetraining is completed, at least one ROI of the diffusion image can beobtained.

It should be noted that the above description regarding the process 800is merely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations or modifications may be madeunder the teachings of the present disclosure. However, those variationsand modifications do not depart from the scope of the presentdisclosure.

FIG. 9 is a flowchart illustrating an exemplary process for extractingat least one target nerve fiber according to some embodiments of thepresent disclosure. At least a portion of process 900 may be implementedon the nerve fiber extraction device 200 as illustrated in FIG. 2 or thenerve fiber extraction device 400 as illustrated in FIG. 4. In someembodiments, one or more operations of the process 900 may beimplemented in the system 100 as illustrated in FIG. 1. In someembodiments, one or more operations in the process 900 may be stored inthe storage device 150 and/or the storage (e.g., the storage 220, etc.)as a form of instructions, and invoked and/or executed by the system100, or a portion thereof (e.g., the processing device 140). In someembodiments, the instructions may be transmitted in a form of electroniccurrent or electrical signals.

In 902, an anatomical image and a diffusion image of a current detectionpart may be obtained.

In 904, at least one ROI of the diffusion image may be extracted basedon the anatomical data, the diffusion data, and the pre-stored ROI maskdata of the current detection part. In some embodiments, the processingdevice 140 may proceed to operation 906 or 908 to determine a maskimage, and then proceed to operation 910 to track the at least onetarget nerve fiber in the mask image. The mask image may include, forexample, a characteristic region mask image or a threshold mask image.

In 906, the characteristic region mask image may be obtained bysegmenting the characteristic region of the diffusion image.

In some embodiments, the characteristic region may include the brain,the chest, etc. Optionally, if the characteristic region is the brain,the segmentation of the characteristic region of the diffusion image maybe implemented by removing a region corresponding to the scalp from thediffusion image, thereby obtaining a brain mask image.

In 908, the threshold mask image may be determined based oncharacteristic data related to the diffusion image. The characteristicdata may be determined based on the diffusion data.

Merely by way of example, the diffusion image may be a DTI image, andthe diffusion data may include the trace of the diffusion tensor andother data. For example, Trace=λ1+λ2+λ3, where λ1, λ2, and λ3 are thethree characteristic directions of the diffusion data, respectively. Thecharacteristic data may include partial anisotropy index FA, VolumeRatio (VR), relative anisotropy index RA, etc., where FA, VR and RA areall calculated from λ1, λ2, and λ3. Therefore, After the feature data isobtained, the threshold of the feature data can be adjusted to determinethe threshold mask image of the diffusion tensor data.

In 910, a preset tracking algorithm may be used to track the at leastone target nerve fiber based on the at least one ROI.

Optionally, the at least one target nerve fiber in the characteristicregion mask image or the threshold mask image can be tracked accordingto a preset tracking algorithm to obtain at least one candidate nervefiber. For instance, the at least one ROI may include at least one firstROI that the at least one target nerve fiber passes through. Theprocessing device 140 may designate one or more candidate nerve fibersthat pass through the at least one first ROI as the target nerve fiber.

It is understandable that the above method may be implemented in aprocessing device. The user can control the tracking of the at least onetarget nerve fiber in the diffusion image by importing configurationfiles and/or inputting instructions to the nerve fiber extraction deviceof the ROI (e.g., the processing device 140). Therefore, the nerve fiberextraction device can determine at least one ROI of the target nervefiber according to the obtained pre-imported configuration file and/orthe received user input instruction. In some embodiments, theconfiguration file and input instructions may include informationrelated to the target nerve fiber(s) that the user wants to track, suchas the name or the classification of the target nerve fiber(s).

It should be noted that the above description regarding the process 900is merely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations or modifications may be madeunder the teachings of the present disclosure. However, those variationsand modifications do not depart from the scope of the presentdisclosure.

FIG. 10 is a schematic diagram illustrating an exemplary method forextracting at least one target nerve fiber according to some embodimentsof the present disclosure. After acquiring the anatomical data of theanatomical image and the diffusion data of the diffusion image, suchdata may be imported into the nerve fiber extraction device. Thecharacteristic data may be determined based on the diffusion data, suchas λ1, λ2, λ3, FA, VR, and RA. In some embodiments, the processingdevice 140 may determine a mask image, and then proceed to track the atleast one target nerve fiber in the mask image. The mask image mayinclude, for example, a characteristic region mask image or a thresholdmask image. Merely by way of example, the subject may be the head, andthe mask image may be obtained by removing a scalp region in thediffusion image. Optionally, the threshold mask image is obtained byadjusting the threshold of the above characteristic data. As anotherexample, the mask image may be obtained by segmenting the diffusionimage. In some embodiments, to determine at least one ROI in thediffusion image, a predetermined ROI mask may be used. For example, theprocessing device 140 may register the anatomical data with thediffusion data to obtain registration information between the anatomicalimage and the diffusion image. The at least one ROI may be determinedbased on the anatomical data, the diffusion data, the predetermined ROImask, and the registration information. Optionally, after the at leastone ROI in the diffusion image is determined, a user may view the ROIinformation on a display of the terminal device and check whether theROI information needs to be updated. The processing device 140 mayobtain a reference user input regarding the ROI information, anddetermines whether to update at least a portion of one or more of the atleast one ROI. If the user decides that at least a portion of the ROIinformation needs to be updated, the user may, for example, adjust theshape, the position, and/or the size of any one of the at least one ROI.If the user decides that none of the ROI information needs to beupdated, the user may input an instruction to the terminal device tocause the processing device 140 to extract at least one target nervefibers from the diffusion image based on the at least one ROI. Moredescriptions regarding the extraction of the at least one target nervefiber may be found elsewhere in the present disclosure, for example, inFIG. 5, FIG. 11, and the descriptions thereof.

FIG. 11 is a flowchart illustrating an exemplary process for extractingat least one target nerve fiber according to some embodiments of thepresent disclosure. At least a portion of process 1100 may beimplemented on the nerve fiber extraction device 200 as illustrated inFIG. 2 or the nerve fiber extraction device 400 as illustrated in FIG.4. In some embodiments, one or more operations of the process 1100 maybe implemented in the system 100 as illustrated in FIG. 1. In someembodiments, one or more operations in the process 1100 may be stored inthe storage device 150 and/or the storage (e.g., the storage 220, etc.)as a form of instructions, and invoked and/or executed by the system100, or a portion thereof (e.g., the processing device 140). In someembodiments, the instructions may be transmitted in a form of electroniccurrent or electrical signals.

In 1102, an anatomical image and a diffusion image of a currentdetection part may be obtained.

In 1104: at least one ROI of the diffusion image may be extracted basedon the anatomical data, the diffusion data, and the pre-stored ROI maskdata of the current detection part.

In 1106, at least one candidate nerve fiber may be determined. Moredetails regarding the determination of the at least one candidate nervefiber may be found elsewhere in the present disclosure, for example, inoperation 910 and the description thereof.

In 1108, a threshold mask image or a characteristic region mask imagemay be obtained. In some embodiments, operation 1108 may be performed ina manner that is similar to operation 906 or 908.

In 1110, at least one ROI is used as a seed point.

In some embodiments, after obtaining the at least one ROI through theabove-mentioned embodiments, the user can directly click the ROI of thediffusion image presented on a terminal device. Alternatively, the usercan select one or more ROIs from a list presented on the terminaldevice. In some embodiments, the user may edit the at least one ROI viathe terminal device. For example, the user may edit the shape of one ormore ROIs. As another example, the user may remove one or more ROIs.

In 1112, at least one target nerve fiber that passes through the atleast one ROI (e.g., a first ROI) may be determined based on the seedpoint. A tracking algorithm may be used to track the at least one targetnerve fiber in the threshold mask image or the characteristic regionmask. The at least one target nerve fiber may be tracked from the seedpoint.

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by this disclosure and arewithin the spirit and scope of the exemplary embodiments of thisdisclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and “some embodiments” mean that a particular feature,structure, or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment” or “one embodiment” or “an alternativeembodiment” in various portions of this specification are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures or characteristics may be combined assuitable in one or more embodiments of the present disclosure.

Further, it will be appreciated by one skilled in the art, aspects ofthe present disclosure may be illustrated and described herein in any ofa number of patentable classes or context including any new and usefulprocess, machine, manufacture, or composition of matter, or any new anduseful improvement thereof. Accordingly, aspects of the presentdisclosure may be implemented entirely hardware, entirely software(including firmware, resident software, micro-code, etc.) or combiningsoftware and hardware implementation that may all generally be referredto herein as a “module,” “unit,” “component,” “device,” or “system.”Furthermore, aspects of the present disclosure may take the form of acomputer program product embodied in one or more computer readable mediahaving computer readable program code embodied thereon.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including electro-magnetic, optical, or thelike, or any suitable combination thereof. A computer readable signalmedium may be any computer readable medium that is not a computerreadable storage medium and that may communicate, propagate, ortransport a program for use by or in connection with an instructionexecution system, apparatus, or device. Program code embodied on acomputer readable signal medium may be transmitted using any appropriatemedium, including wireless, wireline, optical fiber cable, RF, or thelike, or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB. NET,Python or the like, conventional procedural programming languages, suchas the “C” programming language, Visual Basic, Fortran 2003, Perl, COBOL2002, PHP, ABAP, dynamic programming languages such as Python, Ruby, andGroovy, or other programming languages. The program code may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider) or in a cloud computing environment or offered as aservice such as a Software as a Service (SaaS).

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose and that the appended claimsare not limited to the disclosed embodiments, but, on the contrary, areintended to cover modifications and equivalent arrangements that arewithin the spirit and scope of the disclosed embodiments. For example,although the implementation of various components described above may beembodied in a hardware device, it may also be implemented as a softwareonly solution, e.g., an installation on an existing server or mobiledevice.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure aiding in theunderstanding of one or more of the various embodiments. This method ofdisclosure, however, is not to be interpreted as reflecting an intentionthat the claimed subject matter requires more features than areexpressly recited in each claim. Rather, claim subject matter lie inless than all features of a single foregoing disclosed embodiment.

1. A method for image-based nerve fiber extraction, implemented on acomputing device having at least one processor and at least onenon-transitory storage medium, the method comprising: obtaining ananatomical image of a subject and a diffusion image of the subject, thesubject including at least one region of interest (ROI) that relates toextraction of at least one target nerve fiber in the subject;determining, based on the anatomical image, the at least one ROI in thediffusion image; and extracting, from the diffusion image, at least oneof the at least one target nerve fiber based on the at least one ROI. 2.The method of claim 1, wherein the determining, based on the anatomicalimage, the at least one ROI in the diffusion image includes:determining, in the anatomical image, at least one reference regioncorresponding to the at least one ROI; and determining, based on theanatomical image and the at least one reference region, the at least oneROI in the diffusion image.
 3. The method of claim 2, wherein thedetermining, based on the anatomical image and the at least onereference region, the at least one ROI in the diffusion image includes:determining registration information between the anatomical image andthe diffusion image by registering the anatomical image with thediffusion image; and determining, in the diffusion image, the at leastone ROI based on the registration information, the anatomical image, andthe at least one reference region.
 4. The method of claim 2, wherein thedetermining, in the anatomical image, at least one reference regioncorresponding to the at least one ROI includes: obtaining at least onepredetermined ROI mask; and determining, in the anatomical image, the atleast one reference region based on the at least one predetermined ROImask.
 5. The method of claim 2, wherein the determining, in theanatomical image, at least one reference region corresponding to the atleast one ROI includes: obtaining a trained extraction model; anddetermining, in the anatomical image, at least one reference regionusing the trained extraction model.
 6. (canceled)
 7. The method ofclaims 2, wherein the determining, in the anatomical image, at least onereference region corresponding to the at least one ROI includes:obtaining, based on at least one of a default setting or a user input,information related to the at least one target nerve fiber; anddetermining, based on the information related to the at least one targetnerve fiber, the at least one reference region in the anatomical image.8. The method of claim 1, wherein the extracting, from the diffusionimage, at least one of the at least one target nerve fiber based on theat least one ROI includes: identifying, based on the diffusion image, atleast one candidate nerve fiber using a tracking algorithm; andextracting, from the diffusion image, the at least one of the at leastone target nerve fiber selected from the at least one candidate nervefiber.
 9. The method of claim 8, wherein the identifying, based on thediffusion image, at least one candidate nerve fiber using a trackingalgorithm includes: determining a mask image by excluding one or morebackground regions from the diffusion image, the one or more backgroundregions being unrelated to the extraction of the at least one targetnerve fiber; and extracting, from the mask image, the at least onecandidate nerve fiber using the tracking algorithm.
 10. The method ofclaim 9, wherein the determining a mask image includes: determiningcharacteristic data based on the diffusion image; and determining themask image based on the characteristic data.
 11. (canceled)
 12. Themethod of claim 1, wherein the at least one ROI includes at least one ofa first ROI or a second ROI, wherein the at least one target nerve fiberpasses through the first ROI, and the at least one target nerve fiberdoes not pass through the second ROI.
 13. The method of claim 12,wherein the at least one ROI includes the first ROI, and the extracting,from the diffusion image, at least one of the at least one target nervefiber based on the at least one ROI includes: determining at least aportion of the first ROI as a seed point; and extracting, from thediffusion image and based on the seed point, the at least one of the atleast one target nerve fiber that passes through the first ROI using atracking algorithm.
 14. The method of claim 13, wherein the at least oneROI further includes the second ROI, and the extracting, from thediffusion image and based on the diffusion image, the at least one ofthe at least one target nerve fiber that passes through the first ROIusing a tracking algorithm includes: determining, in the diffusion imageand based on the seed point, at least one candidate nerve fiber thatpasses through the first ROI using the tracking algorithm; and for eachof the at least one candidate nerve fiber, determining whether thecandidate nerve fiber passes through the second ROI; and in response todetermining that the candidate nerve fiber does not pass through thesecond ROI, designating the candidate nerve fiber as one of the at leastone target nerve fiber.
 15. (canceled)
 16. A system, comprising: atleast one non-transitory storage medium including a set of instructionsfor image-based nerve fiber extraction; and at least one processor incommunication with the at least one non-transitory storage medium,wherein when executing the set of instructions, the at least oneprocessor is configured to cause the system to perform operationsincluding: obtaining an anatomical image of a subject and a diffusionimage of the subject, the subject including at least one region ofinterest (ROI) that relates to extraction of at least one target nervefiber in the subject; determining, based on the anatomical image, the atleast one ROI in the diffusion image; and extracting, from the diffusionimage, at least one of the at least one target nerve fiber based on theat least one ROI.
 17. The system of claim 16, wherein the determining,based on the anatomical image, the at least one ROI in the diffusionimage includes: determining, in the anatomical image, at least onereference region corresponding to the at least one ROI; and determining,based on the anatomical image and the at least one reference region, theat least one ROI in the diffusion image.
 18. The system of claim 17,wherein the determining, based on the anatomical image and the at leastone reference region, the at least one ROI in the diffusion imageincludes: determining registration information between the anatomicalimage and the diffusion image by registering the anatomical image withthe diffusion image; and determining, in the diffusion image, the atleast one ROI based on the registration information, the anatomicalimage, and the at least one reference region.
 19. The system of claim17, wherein the determining, in the anatomical image, at least onereference region corresponding to the at least one ROI includes:obtaining at least one predetermined ROI mask; and determining, in theanatomical image, the at least one reference region based on the atleast one predetermined ROI mask.
 20. The system of claim 17, whereinthe determining, in the anatomical image, at least one reference regioncorresponding to the at least one ROI includes: obtaining a trainedextraction model; and determining, in the anatomical image, at least onereference region using the trained extraction model. 21-22. (canceled)23. The system of claim 16, wherein the extracting, from the diffusionimage, at least one of the at least one target nerve fiber based on theat least one ROI includes: identifying, based on the diffusion image, atleast one candidate nerve fiber using a tracking algorithm; andextracting, from the diffusion image, the at least one of the at leastone target nerve fiber selected from the at least one candidate nervefiber.
 24. The system of claim 23, wherein the identifying, based on thediffusion image, at least one candidate nerve fiber using a trackingalgorithm includes: determining a mask image by excluding one or morebackground regions from the diffusion image, the one or more backgroundregions being unrelated to the extraction of the at least one targetnerve fiber; and extracting, from the mask image, the at least onecandidate nerve fiber using the tracking algorithm. 25-30. (canceled)31. A non-transitory computer readable medium, comprising at least oneset of instructions, wherein when executed by at least one processor ofa computing device, the at least one set of instructions direct the atleast one processor to perform operations including: obtaining ananatomical image of a subject and a diffusion image of the subject, thesubject including at least one region of interest (ROI) that relates toextraction of at least one target nerve fiber in the subject;determining, based on the anatomical image, the at least one ROI in thediffusion image; and extracting, from the diffusion image, at least oneof the at least one target nerve fiber based on the at least one ROI.